1. Field of the Invention
This invention relates generally to an industrial process, and, more particularly, to various methods and systems for dynamically adjusting metrology sampling based upon available metrology capacity.
2. Description of the Related Art
After a complete reading of the present application, those skilled in the relevant art will understand that the present invention has broad application to a variety of industries involving the manufacture of a variety of different types of devices or workpieces. By way of example only, the background of the application will be discussed in the context of various problems encountered in the manufacture of integrated circuit devices. However, the present invention is not to be considered as limited to use only within the semiconductor manufacturing industry.
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more quickly and more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a lot of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, thermal anneal process tools, implantation tools, etc. The technologies underlying semiconductor process tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the process tools that are currently commercially available suffer certain deficiencies. In particular, some of such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various process tools. The manufacturing tools communicate with a manufacturing frame-work or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing frame-work. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the performance of the devices being fabricated. That is, variations in the fabrication process steps may result in variations of the features that comprise the device as well as device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with performance models to reduce processing variation. Commonly controlled tools include photolithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate with increased profitability.
Target values for the various processes performed are generally based on design values for the devices being fabricated. For example, a particular process layer may have a target thickness. Operating recipes for deposition tools and/or polishing tools may be automatically controlled to reduce variation about the target thickness. In another example, the critical dimensions of a transistor gate electrode may have an associated target value. The operating recipes of photolithography tools and/or etch tools may be automatically controlled to achieve the target critical dimensions.
Typically, a control model is used to generate control actions for changing the operating recipe settings for a process tool being controlled based on feedback or feedforward metrology data collected related to the processing by the process tool. To function effectively, a control model must be provided with metrology data in a timely manner and at a quantity sufficient to maintain its ability to predict the future operation of the process tool it controls.
Within many manufacturing industries great effort is made to insure that processing operations are performed accurately such that the resulting device meets target specifications. This is particularly true within the semiconductor manufacturing industry wherein many metrology tools and sensors are used to acquire a vast amount of metrology data to determine the effectiveness and accuracy of the processing operations performed in a process tool and/or the compliance of the resulting workpiece with product specifications. To that end, a typical semiconductor manufacturing facility may devote a great deal of resources to obtaining such metrology data. Typically, a modern semiconductor manufacturing facility will have many metrology tools or stations where a variety of metrology operations are performed. Illustrative metrology data may include the thickness of a process layer, a critical dimension of a feature formed above a substrate, a planarity of a surface, etc. Some metrology tools are dedicated to performing only one type of metrology operations, e.g., critical dimension measurements, whereas other metrology tools are capable of performing multiple metrology operations. Moreover, a typical semiconductor manufacturing facility may have multiple tools capable of performing the same metrology operation.
In semiconductor manufacturing environments, metrology sampling rates are established for various process operations. The sampling rates may vary depending upon a variety of factors, such as the criticality of the particular process, e.g., gate etch processes, and/or how stable the process operations are in terms of controllability. In semiconductor manufacturing environments, metrology sampling rates are typically set below a level where the aggregate of all of the products selected for sampling would completely utilize all available metrology capacity. This may generally be referred to as baseline sampling rates. The baseline sampling rates are set at less than maximum levels to allow the metrology tools to “catch-up” to accumulated work-in-progress (WIP) after one or more of the metrology tools have been taken out of service for a variety of reasons, e.g., routine maintenance, an unscheduled problem with one of the metrology tools, etc. For example, if one out of four available metrology tools is taken out of service, the work-in-progress (WIP) would slowly accumulate in the metrology queues until the out-of-service metrology tool is returned to service. At that time, all four of the available metrology tools would operate at higher than normal utilization rates until the work-in-progress (WIP) queues are reduced to normal.
One of the problems with the above-described methodology is that it under-utilizes a very valuable resource, i.e., the metrology tools. In general, all other things being equal, as baseline sampling rates are kept higher, more information can be obtained as to how the fabrication facility is operating. The additional metrology information may be used to more quickly identify issues within the fabrication facility that could be degrading yield and/or product performance.
An alternative method of addressing changes in metrology capacity is to maintain sampling rates at very high levels that result in nearly full utilization of all metrology tools under normal production. When one or more metrology tools are taken out of production, the sampling rates may be manually lowered to reduce the amount of work-in-progress (WIP) accumulating in the metrology queues. Under this scheme, when the out-of-service metrology tool(s) is returned to production, the sampling rates are returned to their normally high levels. One problem with this method is that it requires a person to manually reduce the relatively high baseline sampling rates when a metrology tool is taken out of service, and to manually increase the sampling rates back to the relatively high baseline rates when the affected metrology tool is returned to production. This is an inefficient process that requires diligent monitoring of metrology tool capacity by whomever has the authority to adjust the facility's sampling rate plans. If the relatively high baseline sampling rates are not reduced in a timely fashion, work-in-progress (WIP) accumulates in the metrology queues. Moreover, because of the relatively high baseline sampling rates employed in this methodology, when the affected metrology tool is returned to production, there is little excess metrology capacity available to work through the accumulated work-in-progress (WIP). Conversely, if the relatively high baseline sampling rates are not re-established in a timely fashion, production may suffer as the volume of metrology data is reduced, thereby negatively impacting the ability to promptly identify problems within the fabrication facility that may adversely affect production and product yields.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.